B5Fn_CFA
Levi Brackman
22 July 2015
I see myself as someone who:
- Is sometimes rude to others. (Agreeableness)
- Has a forgiving nature.(Agreeableness)
- Is considerate and kind to almost everyone.(Agreeableness)
- Does a thorough job.(Conscientiousness)
- Tends to be lazy. (reverse-scored) (Conscientiousness)
- Does things efficiently.(Conscientiousness)
- Is talkative.(Extraversion)
- Is outgoing, sociable.(Extraversion)
- Is reserved. (reverse-scored) (Extraversion)
- Worries a lot. (Neuroticism)
- Gets nervous easily. (Neuroticism)
- Is relaxed, handles stress well. (reverse-scored) (Neuroticism)
- Is original, comes up with new ideas. (Openness to Experience)
- Values artistic, aesthetic experiences. (Openness to Experience)
- Has an active imagination, is original, comes up with new ideas. (Openness to Experience)
library(lavaan)## This is lavaan 0.5-18
## lavaan is BETA software! Please report any bugs.library(semPlot)
library(dplyr)##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(GPArotation)
library(psych)
library(car)##
## Attaching package: 'car'
##
## The following object is masked from 'package:psych':
##
## logitlibrary(ggplot2)##
## Attaching package: 'ggplot2'
##
## The following object is masked from 'package:psych':
##
## %+%library(GGally)##
## Attaching package: 'GGally'
##
## The following object is masked from 'package:dplyr':
##
## nasaloadthedata
data <- read.csv("~/Psychometric_study_data/allsurveysYT1.csv")
B5<-select(data, B5F_1 , B5F_2 , B5F_3, B5F_4 , B5F_5 , B5F_6 ,B5F_7 , B5F_8 , B5F_9 , B5F_10 , B5F_11 , B5F_12 , B5F_13 , B5F_14 , B5F_15)
B5$B5F_12 <- 9- B5$B5F_12
B5$B5F_9 <- 9- B5$B5F_9
B5$B5F_5 <- 9- B5$B5F_5
B5<-tbl_df(B5)
B5## Source: local data frame [670 x 15]
##
## B5F_1 B5F_2 B5F_3 B5F_4 B5F_5 B5F_6 B5F_7 B5F_8 B5F_9 B5F_10 B5F_11
## 1 2 7 7 4 5 5 3 3 2 2 2
## 2 2 6 7 7 6 7 5 6 4 5 2
## 3 3 7 6 3 2 4 7 5 2 3 5
## 4 5 8 7 8 8 8 7 8 8 2 4
## 5 5 7 5 5 1 5 5 8 6 8 2
## 6 5 3 5 6 7 5 4 5 4 2 4
## 7 2 8 8 5 3 6 7 8 7 2 3
## 8 5 5 7 8 4 7 6 8 5 3 4
## 9 4 5 6 5 5 7 4 5 5 4 6
## 10 1 7 7 7 7 7 6 7 8 2 1
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: B5F_12 (dbl), B5F_13 (int), B5F_14 (int), B5F_15
## (int)View(data)create plots
#ggpairs(B5, columns = 1:15, title="Big 5 Marsh" )create the models
five.model= ' agreeableness =~ B5F_1 + B5F_2 + B5F_3
conscientiousness =~ B5F_4 + B5F_5 + B5F_6
extraversion =~ B5F_7 + B5F_8 + B5F_9
neuroticism =~ B5F_10 + B5F_11 + B5F_12
openness =~ B5F_13 + B5F_14 + B5F_15'
one.model= 'Big5 =~ B5F_1 + B5F_2 + B5F_3 + B5F_4 + B5F_5 + B5F_6 + B5F_7 + B5F_8 + B5F_9 + B5F_10 + B5F_11 + B5F_12 + B5F_13 + B5F_14 + B5F_15'run the models
five.fit=cfa(five.model, data=B5, missing = "fiml")## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 14 15 16 20 27 32 33 56 74 87 88 124 130 138 144 147 151 175 179 196 208 215 216 231 264 265 268 269 272 349 361 363 364 367 374 382 443 444 445 447 448 449 450 452 453 456 457 459 461 463 464 465 467 471 472 473 476 477 478 479 480 481 482 485 488 489 490 491 492 493 495 496 500 502 503 504 505 506 507 509 510 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670## Warning in lavaan::lavaan(model = five.model, data = B5, missing =
## "fiml", : lavaan WARNING: some estimated variances are negative## Warning in lavaan::lavaan(model = five.model, data = B5, missing =
## "fiml", : lavaan WARNING: observed variable error term matrix (theta) is
## not positive definite; use inspect(fit,"theta") to investigate.one.fit=cfa(one.model, data=B5, missing = "fiml")## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
## 14 15 16 20 27 32 33 56 74 87 88 124 130 138 144 147 151 175 179 196 208 215 216 231 264 265 268 269 272 349 361 363 364 367 374 382 443 444 445 447 448 449 450 452 453 456 457 459 461 463 464 465 467 471 472 473 476 477 478 479 480 481 482 485 488 489 490 491 492 493 495 496 500 502 503 504 505 506 507 509 510 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670create pictures
semPaths(five.fit, whatLabels = "std", layout = "tree")## Warning in lavaan(slotOptions = object@Options, slotParTable =
## object@ParTable, : lavaan WARNING: some estimated variances are negative## Warning in lavaan(slotOptions = object@Options, slotParTable =
## object@ParTable, : lavaan WARNING: observed variable error term matrix
## (theta) is not positive definite; use inspect(fit,"theta") to investigate.
semPaths(one.fit, whatLabels = "std", layout = "tree")
#summaries
summary(five.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 80 iterations
##
## Used Total
## Number of observations 431 670
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 438.194
## Degrees of freedom 80
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## agreeableness =~
## B5F_1 1.000 0.430 0.220
## B5F_2 -2.777 0.747 -3.718 0.000 -1.194 -0.726
## B5F_3 -2.639 0.662 -3.983 0.000 -1.135 -0.782
## conscientiousness =~
## B5F_4 1.000 0.964 0.675
## B5F_5 0.508 0.122 4.169 0.000 0.490 0.238
## B5F_6 1.113 0.118 9.406 0.000 1.073 0.783
## extraversion =~
## B5F_7 1.000 1.119 0.597
## B5F_8 1.824 0.316 5.774 0.000 2.041 1.088
## B5F_9 0.217 0.076 2.861 0.004 0.243 0.127
## neuroticism =~
## B5F_10 1.000 1.613 0.754
## B5F_11 0.838 0.107 7.843 0.000 1.352 0.667
## B5F_12 0.564 0.090 6.284 0.000 0.909 0.444
## openness =~
## B5F_13 1.000 1.248 0.807
## B5F_14 0.789 0.072 11.027 0.000 0.984 0.556
## B5F_15 1.053 0.071 14.774 0.000 1.314 0.868
##
## Covariances:
## agreeableness ~~
## conscientsnss -0.239 0.069 -3.476 0.001 -0.576 -0.576
## extraversion -0.145 0.050 -2.936 0.003 -0.302 -0.302
## neuroticism 0.082 0.054 1.512 0.131 0.118 0.118
## openness -0.227 0.065 -3.487 0.000 -0.423 -0.423
## conscientiousness ~~
## extraversion 0.408 0.096 4.276 0.000 0.379 0.379
## neuroticism -0.343 0.113 -3.045 0.002 -0.221 -0.221
## openness 0.568 0.093 6.079 0.000 0.472 0.472
## extraversion ~~
## neuroticism -0.300 0.104 -2.891 0.004 -0.166 -0.166
## openness 0.412 0.121 3.412 0.001 0.295 0.295
## neuroticism ~~
## openness -0.233 0.129 -1.802 0.072 -0.116 -0.116
##
## Intercepts:
## B5F_1 4.186 0.094 44.531 0.000 4.186 2.145
## B5F_2 6.053 0.079 76.413 0.000 6.053 3.681
## B5F_3 6.371 0.070 91.199 0.000 6.371 4.393
## B5F_4 6.211 0.069 90.253 0.000 6.211 4.347
## B5F_5 4.072 0.099 40.994 0.000 4.072 1.975
## B5F_6 6.056 0.066 91.782 0.000 6.056 4.421
## B5F_7 5.608 0.090 62.144 0.000 5.608 2.993
## B5F_8 5.698 0.090 63.062 0.000 5.698 3.038
## B5F_9 3.731 0.092 40.418 0.000 3.731 1.947
## B5F_10 5.060 0.103 49.133 0.000 5.060 2.367
## B5F_11 4.949 0.098 50.716 0.000 4.949 2.443
## B5F_12 3.763 0.099 38.169 0.000 3.763 1.839
## B5F_13 5.893 0.075 79.102 0.000 5.893 3.810
## B5F_14 5.914 0.085 69.380 0.000 5.914 3.342
## B5F_15 6.155 0.073 84.378 0.000 6.155 4.064
## agreeableness 0.000 0.000 0.000
## conscientsnss 0.000 0.000 0.000
## extraversion 0.000 0.000 0.000
## neuroticism 0.000 0.000 0.000
## openness 0.000 0.000 0.000
##
## Variances:
## B5F_1 3.623 0.252 3.623 0.951
## B5F_2 1.279 0.171 1.279 0.473
## B5F_3 0.816 0.144 0.816 0.388
## B5F_4 1.112 0.116 1.112 0.545
## B5F_5 4.012 0.279 4.012 0.944
## B5F_6 0.726 0.116 0.726 0.387
## B5F_7 2.257 0.254 2.257 0.643
## B5F_8 -0.647 0.662 -0.647 -0.184
## B5F_9 3.613 0.246 3.613 0.984
## B5F_10 1.970 0.355 1.970 0.431
## B5F_11 2.277 0.263 2.277 0.555
## B5F_12 3.363 0.266 3.363 0.803
## B5F_13 0.835 0.102 0.835 0.349
## B5F_14 2.164 0.161 2.164 0.691
## B5F_15 0.566 0.102 0.566 0.247
## agreeableness 0.185 0.094 1.000 1.000
## conscientsnss 0.929 0.146 1.000 1.000
## extraversion 1.253 0.272 1.000 1.000
## neuroticism 2.601 0.432 1.000 1.000
## openness 1.557 0.175 1.000 1.000
##
## R-Square:
##
## B5F_1 0.049
## B5F_2 0.527
## B5F_3 0.612
## B5F_4 0.455
## B5F_5 0.056
## B5F_6 0.613
## B5F_7 0.357
## B5F_8 NA
## B5F_9 0.016
## B5F_10 0.569
## B5F_11 0.445
## B5F_12 0.197
## B5F_13 0.651
## B5F_14 0.309
## B5F_15 0.753summary(one.fit, standardized = TRUE, rsquare=TRUE)## lavaan (0.5-18) converged normally after 187 iterations
##
## Used Total
## Number of observations 431 670
##
## Number of missing patterns 1
##
## Estimator ML
## Minimum Function Test Statistic 1042.777
## Degrees of freedom 90
## P-value (Chi-square) 0.000
##
## Parameter estimates:
##
## Information Observed
## Standard Errors Standard
##
## Estimate Std.err Z-value P(>|z|) Std.lv Std.all
## Latent variables:
## Big5 =~
## B5F_1 1.000 0.053 0.027
## B5F_2 -16.232 32.464 -0.500 0.617 -0.867 -0.527
## B5F_3 -14.521 29.015 -0.500 0.617 -0.775 -0.535
## B5F_4 -14.196 28.434 -0.499 0.618 -0.758 -0.531
## B5F_5 -3.023 6.216 -0.486 0.627 -0.161 -0.078
## B5F_6 -14.764 29.563 -0.499 0.617 -0.788 -0.576
## B5F_7 -13.680 27.645 -0.495 0.621 -0.731 -0.390
## B5F_8 -17.999 36.196 -0.497 0.619 -0.961 -0.512
## B5F_9 2.243 4.935 0.454 0.650 0.120 0.062
## B5F_10 2.867 6.012 0.477 0.633 0.153 0.072
## B5F_11 3.659 7.486 0.489 0.625 0.195 0.096
## B5F_12 17.913 35.871 0.499 0.618 0.957 0.467
## B5F_13 -19.386 39.129 -0.495 0.620 -1.035 -0.669
## B5F_14 -16.355 32.986 -0.496 0.620 -0.873 -0.494
## B5F_15 -20.000 40.350 -0.496 0.620 -1.068 -0.705
##
## Intercepts:
## B5F_1 4.186 0.094 44.531 0.000 4.186 2.145
## B5F_2 6.053 0.079 76.413 0.000 6.053 3.681
## B5F_3 6.371 0.070 91.201 0.000 6.371 4.393
## B5F_4 6.211 0.069 90.252 0.000 6.211 4.347
## B5F_5 4.072 0.099 40.994 0.000 4.072 1.975
## B5F_6 6.056 0.066 91.782 0.000 6.056 4.421
## B5F_7 5.608 0.090 62.144 0.000 5.608 2.993
## B5F_8 5.698 0.090 63.063 0.000 5.698 3.038
## B5F_9 3.731 0.092 40.418 0.000 3.731 1.947
## B5F_10 5.060 0.103 49.133 0.000 5.060 2.367
## B5F_11 4.949 0.098 50.716 0.000 4.949 2.443
## B5F_12 3.763 0.099 38.168 0.000 3.763 1.839
## B5F_13 5.893 0.075 79.101 0.000 5.893 3.810
## B5F_14 5.914 0.085 69.379 0.000 5.914 3.342
## B5F_15 6.155 0.073 84.378 0.000 6.155 4.064
## Big5 0.000 0.000 0.000
##
## Variances:
## B5F_1 3.805 0.259 3.805 0.999
## B5F_2 1.953 0.153 1.953 0.722
## B5F_3 1.502 0.118 1.502 0.714
## B5F_4 1.467 0.113 1.467 0.718
## B5F_5 4.226 0.288 4.226 0.994
## B5F_6 1.255 0.102 1.255 0.669
## B5F_7 2.976 0.213 2.976 0.848
## B5F_8 2.595 0.197 2.595 0.737
## B5F_9 3.658 0.249 3.658 0.996
## B5F_10 4.548 0.310 4.548 0.995
## B5F_11 4.066 0.278 4.066 0.991
## B5F_12 3.275 0.244 3.275 0.782
## B5F_13 1.321 0.131 1.321 0.552
## B5F_14 2.369 0.179 2.369 0.756
## B5F_15 1.153 0.123 1.153 0.503
## Big5 0.003 0.011 1.000 1.000
##
## R-Square:
##
## B5F_1 0.001
## B5F_2 0.278
## B5F_3 0.286
## B5F_4 0.282
## B5F_5 0.006
## B5F_6 0.331
## B5F_7 0.152
## B5F_8 0.263
## B5F_9 0.004
## B5F_10 0.005
## B5F_11 0.009
## B5F_12 0.218
## B5F_13 0.448
## B5F_14 0.244
## B5F_15 0.497Residual correlations
correl = residuals(five.fit, type="cor")
correl## $type
## [1] "cor.bollen"
##
## $cor
## B5F_1 B5F_2 B5F_3 B5F_4 B5F_5 B5F_6 B5F_7 B5F_8 B5F_9
## B5F_1 0.000
## B5F_2 0.022 0.000
## B5F_3 -0.059 -0.006 0.000
## B5F_4 0.019 -0.013 0.047 0.000
## B5F_5 -0.286 -0.115 -0.085 0.027 0.000
## B5F_6 0.038 0.038 -0.028 -0.007 0.016 0.000
## B5F_7 0.229 0.041 -0.028 -0.033 -0.187 -0.005 0.000
## B5F_8 0.170 0.050 -0.028 -0.027 -0.053 0.013 0.001 0.000
## B5F_9 -0.130 -0.143 -0.197 -0.121 0.113 -0.079 0.053 0.001 0.000
## B5F_10 0.193 0.049 0.092 0.127 -0.158 0.075 0.144 0.079 -0.232
## B5F_11 0.190 0.061 0.048 0.059 -0.224 -0.001 0.131 0.010 -0.196
## B5F_12 0.058 -0.297 -0.262 -0.259 -0.039 -0.276 -0.110 -0.197 0.066
## B5F_13 0.156 -0.010 -0.012 0.022 -0.056 -0.009 0.098 0.028 -0.084
## B5F_14 0.046 0.061 0.094 0.017 -0.119 -0.006 0.075 -0.010 -0.108
## B5F_15 0.147 0.002 0.009 0.030 -0.103 -0.002 0.116 0.012 -0.055
## B5F_10 B5F_11 B5F_12 B5F_13 B5F_14 B5F_15
## B5F_1
## B5F_2
## B5F_3
## B5F_4
## B5F_5
## B5F_6
## B5F_7
## B5F_8
## B5F_9
## B5F_10 0.000
## B5F_11 0.020 0.000
## B5F_12 0.000 -0.062 0.000
## B5F_13 0.039 -0.017 -0.194 0.000
## B5F_14 0.197 0.202 -0.053 -0.002 0.000
## B5F_15 0.009 0.011 -0.213 0.001 -0.002 0.000
##
## $mean
## B5F_1 B5F_2 B5F_3 B5F_4 B5F_5 B5F_6 B5F_7 B5F_8 B5F_9 B5F_10
## 0 0 0 0 0 0 0 0 0 0
## B5F_11 B5F_12 B5F_13 B5F_14 B5F_15
## 0 0 0 0 0View(correl$cor)
correl1 = residuals(one.fit, type="cor")
correl1## $type
## [1] "cor.bollen"
##
## $cor
## B5F_1 B5F_2 B5F_3 B5F_4 B5F_5 B5F_6 B5F_7 B5F_8 B5F_9
## B5F_1 0.000
## B5F_2 -0.123 0.000
## B5F_3 -0.216 0.280 0.000
## B5F_4 -0.052 -0.011 0.067 0.000
## B5F_5 -0.314 -0.057 -0.020 0.146 0.000
## B5F_6 -0.045 0.062 0.017 0.216 0.157 0.000
## B5F_7 0.199 -0.033 -0.095 -0.088 -0.164 -0.052 0.000
## B5F_8 0.111 0.019 -0.045 -0.021 0.005 0.040 0.451 0.000
## B5F_9 -0.140 -0.083 -0.133 -0.055 0.129 -0.005 0.153 0.171 0.000
## B5F_10 0.211 0.022 0.060 0.053 -0.192 -0.014 0.097 -0.020 -0.252
## B5F_11 0.205 0.054 0.038 0.011 -0.251 -0.061 0.103 -0.061 -0.216
## B5F_12 0.057 -0.088 -0.053 -0.078 -0.025 -0.084 0.029 -0.038 0.027
## B5F_13 0.099 -0.115 -0.103 -0.076 -0.018 -0.096 -0.021 -0.056 -0.012
## B5F_14 0.007 -0.028 0.014 -0.067 -0.095 -0.084 -0.020 -0.085 -0.057
## B5F_15 0.086 -0.103 -0.081 -0.068 -0.061 -0.087 -0.006 -0.071 0.022
## B5F_10 B5F_11 B5F_12 B5F_13 B5F_14 B5F_15
## B5F_1
## B5F_2
## B5F_3
## B5F_4
## B5F_5
## B5F_6
## B5F_7
## B5F_8
## B5F_9
## B5F_10 0.000
## B5F_11 0.516 0.000
## B5F_12 0.301 0.190 0.000
## B5F_13 0.016 -0.015 0.078 0.000
## B5F_14 0.184 0.206 0.149 0.116 0.000
## B5F_15 -0.016 0.012 0.072 0.229 0.132 0.000
##
## $mean
## B5F_1 B5F_2 B5F_3 B5F_4 B5F_5 B5F_6 B5F_7 B5F_8 B5F_9 B5F_10
## 0 0 0 0 0 0 0 0 0 0
## B5F_11 B5F_12 B5F_13 B5F_14 B5F_15
## 0 0 0 0 0View(correl1$cor)zscore correlation anything over 1.96 is going to be statistically significant at the .05 level
zcorrels = residuals(five.fit, type = "standardized")
View(zcorrels$cov)
zcorrels1 = residuals(one.fit, type = "standardized")
View(zcorrels1$cov)Modification indicies
modindices(five.fit, sort. = TRUE, minimum.value = 3.84)## Warning in lavaan(slotOptions = object@Options, slotParTable =
## object@ParTable, : lavaan WARNING: some estimated variances are negative## Warning in lavaan(slotOptions = object@Options, slotParTable =
## object@ParTable, : lavaan WARNING: observed variable error term matrix
## (theta) is not positive definite; use inspect(fit,"theta") to investigate.## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 conscientiousness =~ B5F_12 76.304 -1.020 -0.983 -0.480 -0.480
## 2 agreeableness =~ B5F_12 73.757 2.171 0.933 0.456 0.456
## 3 B5F_10 ~~ B5F_11 65.307 7.977 7.977 1.841 1.841
## 4 B5F_1 ~~ B5F_5 46.850 -1.274 -1.274 -0.317 -0.317
## 5 openness =~ B5F_12 35.015 -0.486 -0.607 -0.296 -0.296
## 6 B5F_11 ~~ B5F_12 28.144 -1.961 -1.961 -0.473 -0.473
## 7 conscientiousness =~ B5F_10 27.052 0.703 0.677 0.317 0.317
## 8 neuroticism =~ B5F_14 26.550 0.284 0.459 0.259 0.259
## 9 B5F_9 ~~ B5F_10 20.297 -0.738 -0.738 -0.180 -0.180
## 10 extraversion =~ B5F_12 19.952 -0.346 -0.387 -0.189 -0.189
## 11 neuroticism =~ B5F_5 19.545 -0.332 -0.535 -0.260 -0.260
## 12 neuroticism =~ B5F_9 18.957 -0.299 -0.483 -0.252 -0.252
## 13 neuroticism =~ B5F_1 18.906 0.301 0.485 0.248 0.248
## 14 B5F_5 ~~ B5F_7 17.291 -0.575 -0.575 -0.149 -0.149
## 15 agreeableness =~ B5F_9 16.409 1.055 0.453 0.237 0.237
## 16 B5F_9 ~~ B5F_12 16.359 0.704 0.704 0.179 0.179
## 17 B5F_1 ~~ B5F_9 16.094 -0.702 -0.702 -0.188 -0.188
## 18 openness =~ B5F_7 15.751 0.346 0.432 0.231 0.231
## 19 agreeableness =~ B5F_10 15.579 -1.114 -0.479 -0.224 -0.224
## 20 B5F_5 ~~ B5F_11 14.947 -0.649 -0.649 -0.155 -0.155
## 21 openness =~ B5F_1 14.744 0.360 0.449 0.230 0.230
## 22 extraversion =~ B5F_1 12.449 0.280 0.314 0.161 0.161
## 23 openness =~ B5F_8 12.375 -0.559 -0.698 -0.372 -0.372
## 24 B5F_1 ~~ B5F_10 11.680 0.565 0.565 0.135 0.135
## 25 neuroticism =~ B5F_7 10.906 0.184 0.297 0.158 0.158
## 26 extraversion =~ B5F_10 10.343 0.283 0.317 0.148 0.148
## 27 B5F_1 ~~ B5F_7 9.977 0.414 0.414 0.113 0.113
## 28 B5F_2 ~~ B5F_12 9.680 -0.378 -0.378 -0.112 -0.112
## 29 B5F_11 ~~ B5F_14 9.463 0.390 0.390 0.109 0.109
## 30 B5F_5 ~~ B5F_9 9.042 0.555 0.555 0.140 0.140
## 31 B5F_1 ~~ B5F_3 8.986 -0.372 -0.372 -0.131 -0.131
## 32 B5F_3 ~~ B5F_9 8.843 -0.311 -0.311 -0.112 -0.112
## 33 B5F_2 ~~ B5F_3 8.666 -1.406 -1.406 -0.589 -0.589
## 34 B5F_4 ~~ B5F_12 8.504 -0.317 -0.317 -0.108 -0.108
## 35 B5F_1 ~~ B5F_11 8.106 0.453 0.453 0.115 0.115
## 36 openness =~ B5F_10 7.551 0.251 0.313 0.147 0.147
## 37 B5F_3 ~~ B5F_4 6.940 0.188 0.188 0.091 0.091
## 38 B5F_5 ~~ B5F_12 6.856 0.485 0.485 0.115 0.115
## 39 B5F_7 ~~ B5F_11 6.852 0.311 0.311 0.082 0.082
## 40 openness =~ B5F_5 6.833 -0.271 -0.338 -0.164 -0.164
## 41 agreeableness =~ B5F_5 6.642 0.952 0.409 0.199 0.199
## 42 B5F_9 ~~ B5F_11 6.531 -0.403 -0.403 -0.104 -0.104
## 43 neuroticism =~ B5F_8 6.262 -0.251 -0.405 -0.216 -0.216
## 44 conscientiousness =~ B5F_9 6.188 -0.302 -0.291 -0.152 -0.152
## 45 extraversion =~ B5F_2 5.825 0.162 0.181 0.110 0.110
## 46 B5F_2 ~~ B5F_6 5.801 0.189 0.189 0.084 0.084
## 47 B5F_2 ~~ B5F_5 5.640 -0.306 -0.306 -0.090 -0.090
## 48 B5F_4 ~~ B5F_10 5.146 0.238 0.238 0.078 0.078
## 49 B5F_5 ~~ B5F_10 5.064 -0.393 -0.393 -0.089 -0.089
## 50 B5F_10 ~~ B5F_14 5.041 0.296 0.296 0.078 0.078
## 51 B5F_3 ~~ B5F_6 4.943 -0.157 -0.157 -0.079 -0.079
## 52 agreeableness =~ B5F_14 4.928 -0.514 -0.221 -0.125 -0.125
## 53 B5F_7 ~~ B5F_15 4.862 0.157 0.157 0.055 0.055
## 54 conscientiousness =~ B5F_8 4.284 0.706 0.681 0.363 0.363
## 55 B5F_7 ~~ B5F_8 4.284 -4.286 -4.286 -1.220 -1.220
## 56 B5F_4 ~~ B5F_6 4.263 -0.608 -0.608 -0.311 -0.311
## 57 B5F_1 ~~ B5F_12 4.232 -0.361 -0.361 -0.090 -0.090
## 58 B5F_3 ~~ B5F_14 4.215 0.173 0.173 0.067 0.067
## 59 B5F_6 ~~ B5F_12 4.190 -0.206 -0.206 -0.073 -0.073
## 60 B5F_2 ~~ B5F_11 3.996 0.222 0.222 0.067 0.067
## 61 openness =~ B5F_9 3.932 -0.167 -0.208 -0.108 -0.108
## 62 neuroticism =~ B5F_15 3.929 -0.082 -0.133 -0.088 -0.088modindices(one.fit, sort. = TRUE, minimum.value = 3.84)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 1 B5F_7 ~~ B5F_8 159.864 1.807 1.807 0.514 0.514
## 2 B5F_13 ~~ B5F_15 152.490 1.003 1.003 0.428 0.428
## 3 B5F_10 ~~ B5F_11 116.822 2.242 2.242 0.518 0.518
## 4 B5F_2 ~~ B5F_3 80.458 0.820 0.820 0.344 0.344
## 5 B5F_10 ~~ B5F_12 53.562 1.404 1.404 0.321 0.321
## 6 B5F_4 ~~ B5F_6 53.253 0.537 0.537 0.275 0.275
## 7 B5F_1 ~~ B5F_5 42.926 -1.266 -1.266 -0.315 -0.315
## 8 B5F_1 ~~ B5F_3 30.940 -0.670 -0.670 -0.237 -0.237
## 9 B5F_14 ~~ B5F_15 28.976 0.518 0.518 0.193 0.193
## 10 B5F_5 ~~ B5F_11 27.780 -1.054 -1.054 -0.252 -0.252
## 11 B5F_9 ~~ B5F_10 27.656 -1.034 -1.034 -0.252 -0.252
## 12 B5F_11 ~~ B5F_14 26.350 0.797 0.797 0.222 0.222
## 13 B5F_11 ~~ B5F_12 21.392 0.840 0.840 0.202 0.202
## 14 B5F_1 ~~ B5F_7 21.043 0.758 0.758 0.207 0.207
## 15 B5F_10 ~~ B5F_14 20.831 0.749 0.749 0.198 0.198
## 16 B5F_9 ~~ B5F_11 20.388 -0.840 -0.840 -0.216 -0.216
## 17 B5F_2 ~~ B5F_13 20.262 -0.414 -0.414 -0.163 -0.163
## 18 B5F_1 ~~ B5F_10 19.349 0.882 0.882 0.211 0.211
## 19 B5F_13 ~~ B5F_14 19.244 0.438 0.438 0.160 0.160
## 20 B5F_2 ~~ B5F_15 18.975 -0.386 -0.386 -0.155 -0.155
## 21 B5F_12 ~~ B5F_14 18.920 0.629 0.629 0.174 0.174
## 22 B5F_8 ~~ B5F_9 18.695 0.668 0.668 0.186 0.186
## 23 B5F_1 ~~ B5F_11 18.369 0.813 0.813 0.206 0.206
## 24 B5F_5 ~~ B5F_6 17.994 0.498 0.498 0.176 0.176
## 25 B5F_3 ~~ B5F_13 16.601 -0.330 -0.330 -0.147 -0.147
## 26 B5F_5 ~~ B5F_10 16.052 -0.847 -0.847 -0.192 -0.192
## 27 B5F_6 ~~ B5F_13 15.783 -0.300 -0.300 -0.141 -0.141
## 28 B5F_6 ~~ B5F_15 15.366 -0.286 -0.286 -0.138 -0.138
## 29 B5F_5 ~~ B5F_7 14.284 -0.659 -0.659 -0.171 -0.171
## 30 B5F_4 ~~ B5F_5 14.133 0.472 0.472 0.160 0.160
## 31 B5F_7 ~~ B5F_9 12.486 0.573 0.573 0.160 0.160
## 32 B5F_3 ~~ B5F_15 11.999 -0.270 -0.270 -0.123 -0.123
## 33 B5F_3 ~~ B5F_9 11.834 -0.407 -0.407 -0.146 -0.146
## 34 B5F_1 ~~ B5F_2 9.877 -0.431 -0.431 -0.134 -0.134
## 35 B5F_1 ~~ B5F_13 9.370 0.364 0.364 0.121 0.121
## 36 B5F_4 ~~ B5F_13 8.923 -0.238 -0.238 -0.108 -0.108
## 37 B5F_8 ~~ B5F_15 8.771 -0.301 -0.301 -0.106 -0.106
## 38 B5F_1 ~~ B5F_9 8.464 -0.523 -0.523 -0.140 -0.140
## 39 B5F_4 ~~ B5F_15 8.253 -0.221 -0.221 -0.102 -0.102
## 40 B5F_12 ~~ B5F_15 8.151 0.320 0.320 0.103 0.103
## 41 B5F_12 ~~ B5F_13 8.132 0.332 0.332 0.105 0.105
## 42 B5F_1 ~~ B5F_15 8.011 0.322 0.322 0.109 0.109
## 43 B5F_1 ~~ B5F_8 7.818 0.441 0.441 0.120 0.120
## 44 B5F_6 ~~ B5F_14 7.453 -0.253 -0.253 -0.104 -0.104
## 45 B5F_3 ~~ B5F_7 7.434 -0.298 -0.298 -0.110 -0.110
## 46 B5F_5 ~~ B5F_9 7.257 0.511 0.511 0.129 0.129
## 47 B5F_6 ~~ B5F_12 7.084 -0.287 -0.287 -0.103 -0.103
## 48 B5F_2 ~~ B5F_12 7.054 -0.352 -0.352 -0.105 -0.105
## 49 B5F_8 ~~ B5F_14 6.567 -0.334 -0.334 -0.101 -0.101
## 50 B5F_4 ~~ B5F_7 6.241 -0.270 -0.270 -0.101 -0.101
## 51 B5F_7 ~~ B5F_11 5.635 0.406 0.406 0.107 0.107
## 52 B5F_5 ~~ B5F_14 5.617 -0.375 -0.375 -0.103 -0.103
## 53 B5F_4 ~~ B5F_12 5.496 -0.270 -0.270 -0.092 -0.092
## 54 B5F_7 ~~ B5F_10 5.001 0.404 0.404 0.101 0.101
## 55 B5F_3 ~~ B5F_4 4.647 0.171 0.171 0.083 0.083
## 56 B5F_8 ~~ B5F_13 4.595 -0.226 -0.226 -0.078 -0.078
## 57 B5F_2 ~~ B5F_9 4.454 -0.284 -0.284 -0.090 -0.090
## 58 B5F_2 ~~ B5F_6 4.341 0.177 0.177 0.079 0.079
## 59 B5F_4 ~~ B5F_14 4.340 -0.205 -0.205 -0.081 -0.081
## 60 B5F_5 ~~ B5F_15 4.087 -0.243 -0.243 -0.078 -0.078Fit Measures
fitmeasures(five.fit)## npar fmin chisq
## 55.000 0.508 438.194
## df pvalue baseline.chisq
## 80.000 0.000 1891.174
## baseline.df baseline.pvalue cfi
## 105.000 0.000 0.799
## tli nnfi rfi
## 0.737 0.737 0.696
## nfi pnfi ifi
## 0.768 0.585 0.802
## rni logl unrestricted.logl
## 0.799 -12086.584 -11867.486
## aic bic ntotal
## 24283.167 24506.803 431.000
## bic2 rmsea rmsea.ci.lower
## 24332.265 0.102 0.093
## rmsea.ci.upper rmsea.pvalue rmr
## 0.111 0.000 0.351
## rmr_nomean srmr srmr_bentler
## 0.372 0.100 0.100
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.106 0.100 0.106
## srmr_mplus srmr_mplus_nomean cn_05
## 0.100 0.106 101.207
## cn_01 gfi agfi
## 111.485 0.986 0.976
## pgfi mfi ecvi
## 0.584 0.660 NAfitmeasures(one.fit)## npar fmin chisq
## 45.000 1.210 1042.777
## df pvalue baseline.chisq
## 90.000 0.000 1891.174
## baseline.df baseline.pvalue cfi
## 105.000 0.000 0.467
## tli nnfi rfi
## 0.378 0.378 0.357
## nfi pnfi ifi
## 0.449 0.385 0.471
## rni logl unrestricted.logl
## 0.467 -12388.875 -11867.486
## aic bic ntotal
## 24867.750 25050.725 431.000
## bic2 rmsea rmsea.ci.lower
## 24907.920 0.157 0.148
## rmsea.ci.upper rmsea.pvalue rmr
## 0.165 0.000 0.427
## rmr_nomean srmr srmr_bentler
## 0.453 0.117 0.117
## srmr_bentler_nomean srmr_bollen srmr_bollen_nomean
## 0.124 0.117 0.124
## srmr_mplus srmr_mplus_nomean cn_05
## 0.117 0.124 47.765
## cn_01 gfi agfi
## 52.300 0.974 0.961
## pgfi mfi ecvi
## 0.649 0.331 NACreate dataset for Target rotation
B5FTR<-select(data, B5F_1 , B5F_2 , B5F_3, B5F_4 , B5F_5 , B5F_6 ,B5F_7 , B5F_8 , B5F_9 , B5F_10 , B5F_11 , B5F_12 , B5F_13 , B5F_14 , B5F_15)
colnames(B5FTR) <- c("1","2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15")
#Target RorationTarg_key <- make.keys(15,list(f1=1:3,f2=4:6, f3=7:9, f4=10:12, f5=13:15))
Targ_key <- scrub(Targ_key,isvalue=1) #fix the 0s, allow the NAs to be estimated
Targ_key <- list(Targ_key)
B5FTR_cor<-corFiml(B5FTR)
out_targetQ <- fa(B5FTR_cor,5,rotate="TargetQ", n.obs = 670, Target=Targ_key) #TargetT for orthogonal rotation
out_targetQ[c("loadings", "score.cor", "TLI", "RMSEA","uniquenesses")]## $loadings
##
## Loadings:
## MR5 MR4 MR3 MR1 MR2
## 1 0.148 0.287 0.210 -0.328
## 2 0.103 0.696
## 3 0.757
## 4 0.124 0.703
## 5 0.232 0.114 0.128 -0.423
## 6 0.659
## 7 0.840
## 8 0.784 0.128
## 9 0.323 -0.247 0.186
## 10 0.798
## 11 0.675
## 12 -0.319 0.128 0.263 0.184
## 13 0.819
## 14 0.547 0.158 0.118
## 15 0.889 -0.116
##
## MR5 MR4 MR3 MR1 MR2
## SS loadings 1.813 1.509 1.478 1.315 1.188
## Proportion Var 0.121 0.101 0.099 0.088 0.079
## Cumulative Var 0.121 0.222 0.320 0.408 0.487
##
## $score.cor
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.00000000 0.05681416 0.31405756 0.35442663 0.3157650
## [2,] 0.05681416 1.00000000 -0.00340393 -0.01917587 -0.2328319
## [3,] 0.31405756 -0.00340393 1.00000000 0.25013556 0.2484601
## [4,] 0.35442663 -0.01917587 0.25013556 1.00000000 0.4091555
## [5,] 0.31576496 -0.23283193 0.24846011 0.40915548 1.0000000
##
## $TLI
## [1] 0.8821429
##
## $RMSEA
## RMSEA lower upper confidence
## 0.06904647 0.05773951 0.07935754 0.10000000
##
## $uniquenesses
## 1 2 3 4 5 6 7
## 0.7703425 0.4669100 0.3791957 0.4777912 0.7703480 0.4649405 0.3011605
## 8 9 10 11 12 13 14
## 0.3079007 0.8147968 0.3946338 0.5192855 0.6559305 0.3503986 0.6279298
## 15
## 0.2370194out_targetQ## Factor Analysis using method = minres
## Call: fa(r = B5FTR_cor, nfactors = 5, n.obs = 670, rotate = "TargetQ",
## Target = Targ_key)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR5 MR4 MR3 MR1 MR2 h2 u2 com
## 1 0.15 0.29 0.21 -0.33 -0.05 0.23 0.77 3.2
## 2 0.02 -0.02 0.10 0.70 -0.01 0.53 0.47 1.0
## 3 0.07 0.00 -0.02 0.76 0.02 0.62 0.38 1.0
## 4 0.06 0.12 -0.01 0.01 0.70 0.52 0.48 1.1
## 5 0.09 0.23 0.11 0.13 -0.42 0.23 0.77 2.1
## 6 0.04 0.02 0.09 0.05 0.66 0.54 0.46 1.1
## 7 0.03 0.07 0.84 -0.02 -0.09 0.70 0.30 1.0
## 8 -0.03 -0.08 0.78 0.05 0.13 0.69 0.31 1.1
## 9 0.06 0.32 -0.25 0.19 0.06 0.19 0.81 2.7
## 10 -0.05 0.80 -0.02 -0.05 0.09 0.61 0.39 1.0
## 11 -0.01 0.68 -0.02 0.03 -0.08 0.48 0.52 1.0
## 12 0.07 -0.32 0.13 0.26 0.18 0.34 0.66 3.1
## 13 0.82 -0.09 0.00 -0.07 0.04 0.65 0.35 1.0
## 14 0.55 0.16 -0.04 0.12 -0.03 0.37 0.63 1.3
## 15 0.89 -0.12 -0.01 -0.03 0.00 0.76 0.24 1.0
##
## MR5 MR4 MR3 MR1 MR2
## SS loadings 1.84 1.51 1.49 1.36 1.26
## Proportion Var 0.12 0.10 0.10 0.09 0.08
## Cumulative Var 0.12 0.22 0.32 0.41 0.50
## Proportion Explained 0.25 0.20 0.20 0.18 0.17
## Cumulative Proportion 0.25 0.45 0.65 0.83 1.00
##
## With factor correlations of
## MR5 MR4 MR3 MR1 MR2
## MR5 1.00 0.09 0.40 0.39 0.39
## MR4 0.09 1.00 0.03 0.04 -0.19
## MR3 0.40 0.03 1.00 0.23 0.26
## MR1 0.39 0.04 0.23 1.00 0.51
## MR2 0.39 -0.19 0.26 0.51 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 105 and the objective function was 4.39 with Chi Square of 2909.95
## The degrees of freedom for the model are 40 and the objective function was 0.25
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.05
##
## The harmonic number of observations is 670 with the empirical chi square 157.61 with prob < 6.9e-16
## The total number of observations was 670 with MLE Chi Square = 165.28 with prob < 3.7e-17
##
## Tucker Lewis Index of factoring reliability = 0.882
## RMSEA index = 0.069 and the 90 % confidence intervals are 0.058 0.079
## BIC = -95.01
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy
## MR5 MR4 MR3 MR1 MR2
## Correlation of scores with factors 0.93 0.87 0.91 0.88 0.86
## Multiple R square of scores with factors 0.86 0.76 0.83 0.77 0.74
## Minimum correlation of possible factor scores 0.72 0.51 0.65 0.53 0.48CFI
1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))## [1] 0.9553362